In the life sciences industry (pharma, agro, food) many products are manufactured in bioreactors, e.g. fermenters, in a batch process. In such cases living micro-organisms in the bioreactor are caused by suitable environmental conditions, such as optimum temperature, pH value and introduction of raw materials and if necessary oxygen, to produce the desired product with the desired quality. Previously these environmental conditions have been measured in the bioreactor as far as possible and have been regulated or controlled in accordance with predetermined empirical values using conventional closed-loop or open-loop controllers. At the end of the batch process the product created is subjected to time-consuming investigations in the laboratory and, depending on its quality, released for further use or disposed of as waste. Since the product quality cannot be measured directly during the ongoing batch process and the micro-organisms as living beings exhibit strong variations as regards their electrophysiological state and their behavior, a large amount of waste has previously been produced.
CPACT News, Edition No. 4, Spring 2002, Page 2, describes, under the heading of “Process Monitoring and Control—A Collaborative CPACT Project”, a method for monitoring and control of batch processes in which a number of process variables, such as temperature, speed of agitation and pH value are measured, from which, by applying the technique of MSPC (Multivariate Statistical Process Control), a fingerprint of the batch process is obtained in the form of a trajectory, i.e. a process graph curve, in a multivariate control chart. By comparing the trajectory of the current batch process with trajectories of earlier processes which have progressed well or badly for known reasons the progress of the current process can be assessed and if necessary timely corrective intervention into the process can be undertaken. In such cases however multivariate control charts only provide starting points for any necessary intervention into the process; the details of how the corrective intervention is to proceed are not specified.
From Helen E. Johnson et al.: “High-Throughput Metabolic Fingerprinting of Legume Silage Fermentations via Fourier Transform Infrared Spectroscopy and Chemometrics” in Applied and Environmental Microbiology, Vol. 70, No. 3, March 2004, pages 1583-1592, it is known that, to determine the fingerprint of silage produced by anaerobic fermentation during the batch process, spectra in the near or mid infrared range are recorded and the volume of data obtained in such cases is compressed by applying PCA (Principal Component Analysis). With principle component analysis a transformation of the high-dimensional spectra into a low-dimensional principle component region is undertaken, in which case as little as possible of the variance, i.e. the information content of the spectra is lost.
It is known from Pia Jorgensen et al.: “On-line batch fermentation process monitoring (NIR)-introducing ‘biological process time’” in Journal of Chemometrics, 2004, No. 18, pages 1-11, that a batch process can be monitored online in a fermenter by recording spectra in the near infrared range, where necessary in conjunction with additional fluorescence analysis or mass spectroscopy. By using Partial Least Squares (PLS) regression or primary component analysis on the spectrum, trajectories are obtained in multivariate control charts, which when compared with trajectories from earlier batch processes, make possible an online assessment of the current process with the option of corrective interventions into the process in the sense of multivariate statistical process control.
The multivariate analysis of batch processes by means of NIR spectrometry and primary component analysis is also known from the publication “The Complete Multivariate Solution for PAT” from Umetrics AB. Here too only online information showing whether the current batch process is running normally or is deviating from its normal course is supplied. In the case of a process which is not running in the optimum way this information merely provides starting points for a manual intervention into the process.
The principle of online control of a fermentation process as a function of the course of a process identified from the spectrum in the near infrared range is known from the Siemens press release entitled “Siemens Process Automation Improves Biotech Production with New Analytical Technology” dated 27 May 2002. The “PathFinder”, “INCA” and “Presto” products from IPCOS are given as software tools to be considered for this purpose. The first two software tools mentioned make it possible to control a process along an optimum trajectory which is determined via a process model. “Presto” is what is known as a soft(ware) sensor which makes use of measuring signals which are not directly related to a desired process variable (target variable) to quantify the target variable by means of a complex mathematical model. Since the physiological state and the behavior of the micro-organisms can hardly be predicted, bioprocesses can barely be modeled or only modeled with great difficulty.